{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands-on 01: Uso de modelos de propagação para análises sistêmicas\n",
    "\n",
    "# Parte 02: Modelagem do Sombreamento\n",
    "\n",
    "### Objetivos\n",
    "As metas desse tutorial são ajudar o usuário na:\n",
    "- Análise visual de potência recebida com sobreamento;\n",
    "- Implementação do sombreamento correlacionado.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prática 01: Sobreamento descorrelacionado\n",
    "\n",
    "Vamos escrever um código para criação do mapa de cobertura (REM) das 7 (sete) estações rádio base (ERBs), como ilustrado na figura a seguir.\n",
    "\n",
    "\n",
    "![fig_hex](../FIGS/HD_01_MATLAB/fig_hex.png)\n",
    "\n",
    "\n",
    "A modelagem tem as seguintes características:\n",
    "\n",
    "- Grid com células hexagonais;\n",
    "- ERBs macrocelulares com altura de 30 m;\n",
    "- Estações móveis com altura média de 1,8 m;\n",
    "- O raio de cada hexágono é um parâmetro ajustável denominado dR;\n",
    "- As dimensões do grid celular com as 7 ERBs é 5dR x 6 $\\sqrt{\\frac{3}{4}}$ dR;\n",
    "- Para fins de definição da Outage (falha da conexão por falta de potência), a sensibilidade do receptor é considerada igual a -104 dBm ([fonte](http://www.comlab.hut.fi/opetus/260/1v153.pdf));\n",
    "- A EIRP (Effective Isotropic Radiated Power) é 57 dBm ([Discussão interessante sobre $P_{TX}$ e EIRP](https://under-linux.org/entry.php?b=1384)). Esse valor é compatível com receptores do GSM ([fonte](https://pt.slideshare.net/naveenjakhar12/gsm-link-budget));\n",
    "- Somente a perda de percurso é considerada como manifestação de canal. Assim, a potência recebida será calculada com o modelo de Okumura-Hata para cidades urbanas grandes;\n",
    "- A frequência da portadora é um parâmetro ajustável denominado dFc;\n",
    "- Para evitar problema numéricos (divisão por zero ou logaritmo de zero), vamos modelar um raio de segurança. Para efeito de cálculo da potência recebida, todos os pontos menores que uma distância denominada dRMin, terão potência recebida igual aquela calculada usando dRMin como distância;\n",
    "- **Sombreamento independente em cada ponto de medição, não importando a distância entre eles.**\n",
    "\n",
    "A ideia é calcular a potência recebida em dBm para pontos equidistantes em toda a ára de cobertura. A distância entre os pontos de medição foi definida como o próximo valor inteiro maior que $\\frac{dR}{20}$. Esse valor pode ser ajustado para melhor visualizar os REMs. \n",
    "\n",
    "**Passo 01:** Crie uma função chamada **fDrawSector.m** com o segunte código. Ela servirá para desenhar um hexágono de centro e raio especificados como parâmetro. Tal função servira para teros certeza que o posicionamento das ERBs estão corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/labsim/EEC1714/fDrawSector.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fDrawSector.m\n",
    "function fDrawSector(dR,dCenter)\n",
    "vtHex=zeros(1,0);\n",
    "for ie=1:6\n",
    "    vtHex=[vtHex dR*(cos((ie-1)*pi/3)+j*sin((ie-1)*pi/3))];\n",
    "end\n",
    "vtHex=vtHex+dCenter;\n",
    "vtHexp=[vtHex vtHex(1)];\n",
    "plot(vtHexp,'k');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 02:** Para testar a função, vamos criar um hexágono centrado no ponto (100,50) e com raio 100. Para isso execute o seguinte comando no Matlab (você precisa colocar o arquivo **fDrawSector.m** na pasta de trabalho do Matlab).   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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aIwCgOOQGKZvNXr9+/cKFCw899FBvb++uXbt+8YtfKKUMw8hkMlVVVdbDfD5fMBicmppa9V/kpvcXALzGTRfu5F6yy+VySqm///3vg4ODmqZdunTpkUceue+++zZv3qyUCofD+UdqmpbNZuf9IdFo1NrQdX3eB1AjAE63+IW7/GlQPrlB8vv9Pp/vwQcf1DRNKdXQ0BAKhd55551PfvKTSqnx8fGGhgbrkdPT04FAYN4fslCHAMAj8qdB+WWSe8nO5/PV1NTMHH2smUnTtMrKysnJSWtnKpUyDKOmpmYVfwXjEQB3cMeFO7lBUkq1tLS89tprt2/fVkq98cYbt2/frqurs/b39vam02mlVHd3d11dXXV19Up/ODUC4CYuaJLcS3ZKqW9+85t//vOfv/CFL9x1110ffvjhT3/607vvvlsp1dbWput6Y2NjRUVFKBTq6emxe6UAgEK5eUqIRqN8lwGApyz+7QbhH6uLvmQHAPAOLwaJ8QiAWzn6kyTPBYkaAXA35zbJc0ECAMjkrSAxHgHwAocOSR4KEjUC4B1ObJKHggQAkMwrQWI8AuA1jhuSPBEkagTAm5zVJE8ECQAgn/uDxHgEwMscNCS5PEjUCACc0iSXBwkA4BRuHiAYjwAgT/4p0c0T0rZt2xwxpQJAqa1Zs2bbtm12r2IJbg4SAMBBXB4kp3yUBwClI/9incXlQVI0CYC3OaVGygtBAgA4gieCxJAEwJscNB4pjwRJ0SQA3uOsGinvBAkAIJyHgsSQBMA7HDceKU8FSdEkAN7gxBoprwUJACCW54LEkATA3Rw6HikPBknRJADu5dwaKW8GCQAg0Fq7F7Aso6Oj77777r333rtx40ZrTzKZHBgYMAwjFovFYrGV/kBrSHLu+wgAmMvppzUHTEipVOqpp546evToe++9Z+3RdT0ej0cikfr6+kQi0dfXZ+8KAQCFc0CQjh49+uSTT87c09XV1dra2t7e/vDDDz/33HPPP/98Nptd6Y/lkyQAbuL08UjJD9K5c+eUUnv27Jm5c3h4uKmpydpubm5Op9MjIyOr+OE0CYA7uKBGSvhnSO+///6JEydeffXVmTsNw8hkMlVVVdZNn88XDAanpqbm/QnRaNTa0HW9pEsFAJnyp0H5RAcpkUgcPHgwEomYppnfab0LCIfD+T2api10yW7JDvHtBgBOt/hJLH8alF8muZfs3n777UuXLn3iE58YGhr64x//qJS6cuVKMpnUNE0pNT4+nn/k9PR0IBBY9V/EhTsAzuWmt9RyJyS/3799+/bTp0+r/0xFg4ODwWBw69atlZWVk5OT1sNSqZRhGDU1NXauFQBQMLlBamhoaGhosLZN06ytre3s7LT2tLS09Pb23n///evWrevu7q6rq6uuri7k7+LCHQAnctmJS26QFtHW1qbremNjY0VFRSgU6unpKfxn0iQAzuK+U5YzgqRp2syvJ2iadvLkSRvXAwAoOrlfaig/vt0AwCncNx4pgjQLTQIgnytrpAgSAEAIgjQbQxIAydw6HimCNC+aBEAmF9dIESQAgBAEaX4MSQCkcfd4pAjSImgSADlcXyNFkAAAQhCkxTAkAZDAC+ORIkhLokkA7OWRGimCBAAQgiAtjSEJgF28Mx4pgrRMNAlA+XmqRoogAQCEIEjLxZAEoJy8Nh4pggQAEIIgrQBDEoDy8OB4pAjSStEkAKXmzRopggQAEIIgrRhDEoDS8ex4pAjS6tAkAKXg5RopggQAEIIgrRJDEoDi8vh4pAhSIWgSgGKhRoogAQCEIEgFYUgCUDjGI8tauxewmGQyefHixYmJiWAwuHfv3h07dsy8a2BgwDCMWCwWi8VsXKTVJJ5MAFaHE0ie6AmptbV1YmJi586dmqYdOHDg7Nmz1n5d1+PxeCQSqa+vTyQSfX199q4TAFA40RPS4OBgKBSyttevX//SSy+1tLQopbq6ulpbW9vb25VSmzZt6ujo2L9/v9/vt2udDEkAVodTx0yiJ6R8jZRS4XA4k8lY28PDw01NTdZ2c3NzOp0eGRmxYX0z8GESgJWiRrOInpDyTNPs7+/ft2+fUsowjEwmU1VVZd3l8/mCweDU1NS8fzAajVobuq6XZ6kAIEr+NCifM4J0+PDhDRs2WNforDcU4XA4f6+madlsdt4/WM4OceEOwPKV7XSRPw3KL5PoS3aWzs7Omzdvvvzyy9anRJqmKaXGx8fzD5ieng4EAratbwYu3AFYDt68zkv6hHTkyJFr16719/cHg0Frj6ZplZWVk5OT1s1UKmUYRk1NjX1rBAAUgegJ6dixY1evXj116lQgEDBN0zRNa39LS0tvb286nVZKdXd319XVVVdX27rS/2JIArA4xqOFiJ6Qzpw5o5TatWuXdVPTtLGxMaVUW1ubruuNjY0VFRWhUKinp8fOVc7Bh0kAFsLJYRGig7TQVxI0TTt58mSZFwMAKCnRl+yciwt3AOZiPFocQSoVmgRgJmq0JIIEABCBIJUQQxIAC+PRchCk0qJJAKjRMhEkAIAIBKnkGJIAL2M8Wj6CBAAQgSCVA0MS4E2MRytCkMqEJgFeQ41WiiABAEQgSOXDkAR4B+PRKhCksqJJgBdQo9UhSAAAEQhSuTEkAe7GeLRqBMkGNAlwK2pUCIIEABCBINmDIQlwH8ajAhEk29AkwE2oUeEIEgBABIJkJ4YkwB0Yj4qCINmMJgFOR42KhSABAEQgSPZjSAKci/GoiAiSCDQJcCJqVFwECQAgAkGSgiEJcBbGo6Jba/cCVimZTA4MDBiGEYvFYrGY3cspDqtJPMUB+XiploIjJyRd1+PxeCQSqa+vTyQSfX19dq8IAFAoR05IXV1dra2t7e3tSqlNmzZ1dHTs37/f7/fbva4iYEgC5ONFWiKOnJCGh4ebmpqs7ebm5nQ6PTIyYu+SiogPkwDJqFHpOG9CMgwjk8lUVVVZN30+XzAYnJqamvfB0WjU2tB1vUzrAwBJ8qdB+ZwXJOu9STgczu/RNC2bzc77YId2iAt3gExOfGHmT4Pyy+S8S3aapimlxsfH83ump6cDgYB9KwIAFIEjg1RZWTk5OWndTKVShmHU1NTYu6qi45MkQBonjkfO4rwgKaVaWlp6e3vT6bRSqru7u66urrq62u5FFR9NAuSgRmXgvM+QlFJtbW26rjc2NlZUVIRCoZ6eHrtXBAAolCODpGnayZMn7V5FOfDtBkACXobl4chLdp7ChTvAXtSobAgSAEAEguQADEmAXRiPyokgOQNNAsqPGpUZQQIAiECQHIMhCSgnxqPyI0hOQpOA8qBGtiBIAAARCJLDMCQBpcZ4ZBeC5Dw0CSgdamQjggQAEIEgORJDElAKjEf2IkhORZOA4qJGtiNIAAARCJKDMSQBxcJ4JAFBcjaaBBSOGglBkAAAIhAkx2NIAgrBeCQHQXIDmgSsDjUShSABAEQgSC7BkASsFOORNATJPWgSsHzUSCCCBAAQgSC5CkMSsByMRzIRJACACATJbRiSgMUxHolFkFyIJgELoUaSrbV7AYtJJpMXL16cmJgIBoN79+7dsWPHzLsGBgYMw4jFYrFYzMZFAgCKQvSE1NraOjExsXPnTk3TDhw4cPbsWWu/ruvxeDwSidTX1ycSib6+PnvXKRBDEjAX45FwoiekwcHBUChkba9fv/6ll15qaWlRSnV1dbW2tra3tyulNm3a1NHRsX//fr/fb+da5bGaxMsPsPBykE/0hJSvkVIqHA5nMhlre3h4uKmpydpubm5Op9MjIyM2rA8AUDyiJ6Q80zT7+/v37dunlDIMI5PJVFVVWXf5fL5gMDg1NTXvH4xGo9aGruvlWaooDEmAxcsvhPxpUD5BQcrlctls1trWNG3mXYcPH96wYYN1jc56VoXD4fy9mqbl/+As3uzQTDQJ8PhLIH8alF8mQZfsLly4UPcfpmnm93d2dt68efPll1+2PiWyWjU+Pp5/wPT0dCAQKP+CAQBFJGhC2r179+7du2ftPHLkyLVr1/r7+4PBoLVH07TKysrJyUnrZiqVMgyjpqamrGt1FIYkeBlPfgcRNCHNdezYsatXr546dSoQCJimmR+bWlpaent70+m0Uqq7u7uurq66utrWlUrHt8DhTdTIWQRNSHOdOXNGKbVr1y7rpqZpY2NjSqm2tjZd1xsbGysqKkKhUE9Pj52rBAAUg+ggLfSVBE3TTp48WebFOB0X7uA1POEdR/QlOxQXF+7gHdTIiQgSAEAEguQtDEnwAsYjhyJInkOT4G7UyLkIEgBABILkRQxJcCvGI0cjSB5Fk+A+1MjpCBIAQASC5F0MSXATxiMXIEieRpPgDtTIHQgSAEAEguR1DElwOsYj1yBIAAARCBIYkuBgjEduQpCgFE2CM1EjlyFIAAARCBL+jSEJzsJ45D4ECf9Fk+AU1MiVCBIAQASChP/BkAT5GI/ciiBhNpoEyaiRixEkAIAIBAnzYEiCTIxH7kaQMD+aBGmokesRJACACGvtXgDkYkiCKIxHrkeQsBhOAQDKxhlBGh0dfffdd++9996NGzdae5LJ5MDAgGEYsVgsFovZuzwAQOEc8BlSKpV66qmnjh49+t5771l7dF2Px+ORSKS+vj6RSPT19dm7QheLRqN2L8HxOIZFwWH0AgcE6ejRo08++eTMPV1dXa2tre3t7Q8//PBzzz33/PPPZ7NZu5YHACgK6UE6d+6cUmrPnj0zdw4PDzc1NVnbzc3N6XR6ZGTEhsUBAIpH9GdI77///okTJ1599dWZOw3DyGQyVVVV1k2fzxcMBqempuxYIACgaAQFKZfL5a+8aZqmlEokEgcPHoxEIqZp5h9mfe8rHA7n92iattAlO647F45jWDiOYVFwGF1PUJAuXLjQ2dlpbV+5cmV0dPTSpUv79u0bGhrKZDLWzvXr13/qU59SSo2Pjzc0NFgPnp6eDgQCc3+grutlWjoAoGCCgrR79+7du3fnb/r9/u3bt58+fVr9ZyoaHBwMBoNbt26trKycnJy0HpZKpQzDqKmpsWXNAIBiccbvhjJNs7a29vTp09ZU9MILLwwODr722mvr1q374Q9/+M477/zqV7+ye40AgIIImpCWr62tTdf1xsbGioqKUCjU09Nj94oAAIVyxoQEAHA96f8dEgDAIwgSAEAER36GtCR+9erqXLp06a9//Wv+ZkNDg/Ule8UhXVQulxsdHb1+/Xo2m43H4zPvWui4cTznWugw8rRcvmQyefHixYmJiWAwuHfv3h07dsy8S/5T0f/ss8/au4Ki03X9wQcf/PKXv7xly5af/OQnfr//85//vN2LcoaXXnrpd7/7XS6Xu3Hjxo0bN7Zs2bJ582bFIV3KD37wg+eff/7GjRtnzpxpb2/P71/ouHE857XQYeRpuXy7d+++6667du7c+cEHHyQSicrKyk9/+tPKQU/FO67zrW9960c/+pG1/eabb37uc5/LZDL2Lskpjh49evTo0bn7OaSL++ijj+7cufPmm29u37595v6FjhvHc14LHUaelst369at/PYLL7zw1a9+1dp2ylPRhZ8h8atXC5FOp99666133nln5k4O6eKs33Q110LHjeM5r4UOo+JpuWyhUCi/HQ6Hrd9xo5zzVHTbZ0j86tUCXbx48caNG2NjY5FIpKenp7q6mkO6OgsdN47nKvC0XCnTNPv7+/ft26cc9VR024R0ZyW/ehWzdHR0/OlPf3rllVcuX768bdu2J554QnFIV2uh48bxXCmelqtw+PDhDRs2WB/FOeip6LYgWVP/+Ph4fs9Cv3oVc+X/D/GaprW3t1+7ds0wDA7p6ix03DieK8XTcqU6Oztv3rz58ssv+/1+5ainotsu2Wmaxq9eLYqPPvpIKbV27VoO6eosdNw4noXgabmkI0eOXLt2rb+/PxgMWnsc9FR024SklGppaent7U2n00qp7u7uurq66upquxflDPnPMz/44IMXX3zxM5/5jPUeikO6uFwuZ5qm9QGyaZr5/33XQseN4zmvhQ4jT8vlO3bs2NWrV0+dOhUIBJz4VHTh77IzTfM73/nOW2+9lf/Vq3fffbfdi3KGL37xi7du3QoEAh9++OGOHTt+9rOfRSIRxSFdyu9///tDhw7N3DM2NqZp2kLHjeM5r4UOI0/L5Zv1/zDUBi426gAAAL5JREFUNG1sbEwtfKykHUMXBskyNTV169YtnqArZZrm2NhYbW3t3O/gckhXZ6HjxvFcPp6WRSH/qejaIAEAnMWFnyEBAJyIIAEARCBIAAARCBIAQASCBAAQgSABAEQgSAAAEQgSAEAEggQAEIEgAQBEIEgAABEIEgBABIIEABCBIAEARCBIAAARCBIAQASCBAAQgSABAEQgSAAAEQgSAEAEggQAEIEgAQBEIEgAABEIEgBABIIEABCBIAEARPh/+40bqPj6DOcAAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fDrawSector(100,100+50*i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 03:** Crie uma função chamada **fDrawDeploy.m** com o segunte código. Ela servirá para desenhar o grid celular. Tal função servira para teros certeza que o posicionamento das ERBs estão corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/labsim/EEC1714/fDrawDeploy.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fDrawDeploy.m\n",
    "function fDrawDeploy(dR,vtBs)\n",
    "% Desenha setores hexagonais\n",
    "hold on;\n",
    "for iBsD = 1 : length(vtBs)\n",
    "    fDrawSector(dR,vtBs(iBsD));\n",
    "end\n",
    "% Plot BSs\n",
    "plot(vtBs,'sk'); axis equal;\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 04:** Inspecione e insira o código a seguir no editor do Matlab (salve com o nome **handson2_P2_1.m**). Nesse código, vamos criar um vetor com a posição das 7 ERBs. A posição é ajustada para que a referência, i.e., o ponto (0,0) seja o canto inferior esquerdo do grid. Também já criamos o grid com a dimensão especificada no hands-on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dR = 5e3; % Raio do Hexágono\n",
    "dIntersiteDistance = 2*sqrt(3/4)*dR;                       % Distância entre ERBs (somente para informação)\n",
    "dDimX = 5*dR;                                              % Dimensão X do grid\n",
    "dDimY = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "% Vetor com posições das BSs (grid Hexagonal com 7 células, uma célula central e uma camada de células ao redor)\n",
    "vtBs = [ 0 ];\n",
    "dOffset = pi/6;\n",
    "for iBs = 2 : 7\n",
    "    vtBs = [ vtBs dR*sqrt(3)*exp( j * ( (iBs-2)*pi/3 + dOffset ) ) ];\n",
    "end\n",
    "vtBs = vtBs + (dDimX/2 + j*dDimY/2);                        % Ajuste de posição das bases (posição relativa ao canto inferior esquerdo)\n",
    "\n",
    "% Desenha setores hexagonais\n",
    "fDrawDeploy(dR,vtBs)\n",
    "axis equal;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prática 02: Cálculo e plot da potência recebida sem e com shadowing\n",
    "\n",
    "Vamos escrever um código para o cálculo da potência recebida nos pontos de medição do REM de cada ERB, e também considerando a composição das 7 ERBs. Como especificado no hands-on, precisamos considerar que a potência recebida de cada ponto de medição é a maior potência recebida em relação às 7 ERBs.\n",
    "\n",
    "**Passo 01:** Inspecione, insira o código a seguir no editor do Matlab (salve com o nome **handson2_p21.m**). Nesse código, vamos:\n",
    "\n",
    "- Criar sete matrizes de distâncias relativas de cada ponto de medição e para cada ERB (matrizes **mtDistEachBs**). Aplicaremos o raio de segurança a essas distâncias;\n",
    "- Com as distâncias, usaremos o modelo de Okumura-Hata para calcular a perda de percurso (matrizes **mtPldB**);\n",
    "- Sortearemos baseado em uma distribiução Lognormal amostras independentes do Sombreamento para cada ponto de medição (matrizes **mtShadowing**);\n",
    "- Com as matrizes de EIRP, da perda de percurso e do sombreamento, calcularemos a potência recebida de cada ERB em cada ponto de medição. Para cada ERB montaremos a matriz **mtPowerEachBSdBm**;\n",
    "- Montaremos uma única matriz **mtPowerFinaldBm** com a maior potência recebida em cada ponto de medição;\n",
    "- Plotaremos o REM de cada ERB e da composição das 7 ERBs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% Entrada de parâmetros\n",
    "dR = 1e3;                                                  % Raio do Hexágono\n",
    "dFc = 800;                                                 % Frequência da portadora\n",
    "dSigmaShad = 8;                                            % Desvio padrão do sombreamento lognormal\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "dPasso = ceil(dR/20);                                      % Resolução do grid: distância entre pontos de medição\n",
    "dRMin = dPasso;                                            % Raio de segurança\n",
    "dIntersiteDistance = 2*sqrt(3/4)*dR;                       % Distância entre ERBs (somente para informação)\n",
    "dDimX = 5*dR;                                              % Dimensão X do grid\n",
    "dDimY = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "dPtdBm = 57;                                               % EIRP (incluindo ganho e perdas) (https://pt.slideshare.net/naveenjakhar12/gsm-link-budget)\n",
    "dPtLinear = 10^(dPtdBm/10)*1e-3;                           % EIRP em escala linear\n",
    "dHMob = 5;                                                 % Altura do receptor\n",
    "dHBs = 30;                                                 % Altura do transmissor\n",
    "dAhm = 3.2*(log10(11.75*dHMob)).^2 - 4.97;                 % Modelo Okumura-Hata: Cidade grande e fc  >= 400MHz\n",
    "% Vetor com posições das BSs (grid Hexagonal com 7 células, uma célula central e uma camada de células ao redor)\n",
    "vtBs = [ 0 ];\n",
    "dOffset = pi/6;\n",
    "for iBs = 2 : 7\n",
    "    vtBs = [ vtBs dR*sqrt(3)*exp( j * ( (iBs-2)*pi/3 + dOffset ) ) ];\n",
    "end\n",
    "vtBs = vtBs + (dDimX/2 + j*dDimY/2);                        % Ajuste de posição das bases (posição relativa ao canto inferior esquerdo)\n",
    "%\n",
    "% Matriz de referência com posição de cada ponto do grid (posição relativa ao canto inferior esquerdo)\n",
    "dDimY = ceil(dDimY+mod(dDimY,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimX = ceil(dDimX+mod(dDimX,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "[mtPosx,mtPosy] = meshgrid(0:dPasso:dDimX, 0:dPasso:dDimY);\n",
    "% Iniciação da Matriz de com a pontência de recebida máxima em cada ponto\n",
    "% medido. Essa potência é a maior entre as 7 ERBs.\n",
    "mtPowerFinaldBm = -inf*ones(size(mtPosy));\n",
    "mtPowerFinalShaddBm = -inf*ones(size(mtPosy));\n",
    "% Calcular O REM de cada ERB e aculumar a maior potência em cada ponto de medição\n",
    "for iBsD = 1 : length(vtBs)                                 % Loop nas 7 ERBs\n",
    "    % Matriz 3D com os pontos de medição de cada ERB. Os pontos são\n",
    "    % modelados como números complexos X +jY, sendo X a posição na abcissa e Y, a posição no eixo das ordenadas\n",
    "    mtPosEachBS = (mtPosx + j*mtPosy)-(vtBs(iBsD));\n",
    "    mtDistEachBs = abs(mtPosEachBS);              % Distância entre cada ponto de medição e a sua ERB\n",
    "    mtDistEachBs(mtDistEachBs < dRMin) = dRMin;             % Implementação do raio de segurança\n",
    "    % Okumura-Hata (cidade urbana) - dB\n",
    "    mtPldB = 69.55 + 26.16*log10(dFc) + (44.9 - 6.55*log10(dHBs))*log10(mtDistEachBs/1e3) - 13.82*log10(dHBs) - dAhm;\n",
    "    % Shadowing independente em cada ponto\n",
    "    mtShadowing = dSigmaShad*randn(size(mtPosy));\n",
    "    % Potências recebidas em cada ponto de medição sem shadowing\n",
    "    mtPowerEachBSdBm = dPtdBm - mtPldB;           \n",
    "    % Potências recebidas em cada ponto de medição com shadowing\n",
    "    mtPowerEachBSShaddBm = dPtdBm - mtPldB + mtShadowing;           \n",
    "    % Cálulo da maior potência em cada ponto de medição sem shadowing\n",
    "    mtPowerFinaldBm = max(mtPowerFinaldBm,mtPowerEachBSdBm);\n",
    "    % Cálulo da maior potência em cada ponto de medição com shadowing\n",
    "    mtPowerFinalShaddBm = max(mtPowerFinalShaddBm,mtPowerEachBSShaddBm);\n",
    "end\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) sem shadowing\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinaldBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB sem shadowing']);\n",
    "%\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) sem shadowing\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinalShaddBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB com shadowing']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A execução do código resulta em:**\n",
    "1. Dois mapas REMs mostrando o grid celular e a potência recebida nos pontos de medição para as 7 ERBS;\n",
    "2. Foi utilizado um colormap diferente da primeria parte do hands-on para diferenciar melhor os níveis de potência e as diferentes cores que o representa;\n",
    "2. A situação com e sem sombreamento estão identificadas no título de cada gráfico;\n",
    "3. Note que a distribuição de potência deixa de ser uniforme e radial quando o sombreamento está presente.\n",
    "\n",
    "** Analise o código com cuidado. Tente compreender a modelagem e a sintaxe usada. Discuta com os colegas. Faça um debug usando a IDE do Matlab.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sombreamento correlacionado\n",
    "\n",
    "Medidas em vários ambientes de comunicação móveis têm mostrado que o desvanecimento de larga escala do canal de propagação rádio-móvel entre um usuário e as diversas ERBs da rede é correlacionado [fonte](https://ieeexplore.ieee.org/document/1105). O grau de correlação não depende somente da separação entre as estações-bases, mas também da configuração dos espalhadores em volta do usuário móvel. \n",
    "    \n",
    "A equação a seguir propõe um modelo para o sombreamento correlacionado, no qual o sombreamento entre um usuário\n",
    "$i$ (ou um ponto de medição $i$) e uma ERB $j$ é composto de duas parcelas separadas: (i) Uma componente do ambiente; (ii) outra componente que depende do caminho entre receptor e transmissor (ERB e ponto de medição). \n",
    "\n",
    "\\begin{equation}\n",
    "    X_{\\sigma _{i,j} }  = \\sqrt \\rho  X_{ST_i }  + \\sqrt {1 - \\rho }\n",
    "    X_{MBP_{i,j} }\n",
    "\\end{equation}\n",
    "\n",
    "sendo $X_{ST_i }$ a componente de sombreamento do ambiente, e $X_{MBP_{i,j} }$ a componente de sombreamento\n",
    "dependente do caminho entre receptor e transmissor. A variável $\\rho$ é o coeficiente de correlação do sombreamento entre ERB. Quando $\\rho$ = 1, não existe correlação do sombreamento entre diferentes ERBs (o sombreamento de cada ERB é independente). Por outro lado, quando $\\rho$ = 0, o sombreamento é igual para um ponto do espçao e qualquer ERB do sistema.\n",
    "\n",
    "As contribuições da **componente do ambiente** são condicionadas somente à posição do usuário (ou ponto de medição), enquanto que  as **contribuições da componente dependente\n",
    "do caminho entre receptor e transmissor** são dependentes tanto da posição dos usuários (ponto de medição), como da ERB em que ele está conectado.\n",
    "\n",
    "\n",
    "Para estabelecer o modelo em duas dimensões faz-se necessário gerar um regra de correlação espacial entre as amostras de sombreamento utilizadas na equação acima. Nesse modelo, a área de cobertura é mapeada em grades quadradas, e cada ponto de intersecção dos quadrados (pontos de grade) tem amostras de sombreamento independentes. A figura a seguir ilustra essa modelagem.\n",
    "\n",
    "\n",
    "![fig_shadcorr_mod](../FIGS/HD_01_MATLAB/fig_shadcorr_mod_300.png)\n",
    "\n",
    "Assim, vamos construir um mapa 2D de amostras independentes de sombreamento para cada ERB. Cada mapa cobre toda área de cobertura simulada e dele é extraída a componente de sombreamento dependente do caminho entre receptor e transmissor ($X_{MBP_{i,j} }$). \n",
    "\n",
    "Para obter as amostras de sombreamento referente à componente do ambiente, um\n",
    "mapa independente adicional é construído. Esse mapa é compartilhado por todos os usuários do sistema, e dele é obtido a\n",
    "amostra $X_{ST_i }$ para cada usuário (ou ponto de medição) em questão.\n",
    "\n",
    "As amostras de sombreamento para pontos de medição entre **pontos de grade** são correlacionadas com as amostras de sombreamento dos quatro pontos de grade mais próximos e calculadas por meio de uma interpolação linear. \n",
    "\n",
    "Por exemplo, a amostra de sombreamento para o usuário ilustrado na figura a seguir, em relação à uma\n",
    "ERB qualquer, é mostrada na equação a seguir ([fonte](https://vtechworks.lib.vt.edu/handle/10919/29051)).\n",
    "\n",
    "![fig_shadcorr_mod_zoom_300](../FIGS/HD_01_MATLAB/fig_shadcorr_mod_zoom_300.png)\n",
    "\n",
    "\\begin{equation}\n",
    "    Xu = \\left( {1 - Y} \\right)\\left[ {X_A \\left( {1 - X} \\right) +\n",
    "    X_B \\left( X \\right)} \\right] + \\left( Y \\right)\\left[ {X_C \\left(\n",
    "    {1 - X} \\right) + X_D \\left( X \\right)} \\right]\n",
    "\\end{equation}\n",
    "\n",
    "sendo $X_{A}$, $X_{B}$, $X_{C}$ e $X_{D}$ as amostras de sombreamento dos quatro pontos mais próximos do usuário (ponto de medição) em questão; $X$ e $Y$ são as distâncias horizontal e vertical entre o usuário (ponto de medição) e o ponto A, respectivamente. $X$ e $Y$ são normalizadas pela distância de descorrelação, assumindo valores entre 0 e 1. A distância de descorrelação $d_{dec}$ é a separação mostrada nas figuras anteriores, que é medida como a menor distância entre dois pontos de grade. Essa distância representa a separação física na qual duas amostras de sombreamento podem ser consideradas independentes.\n",
    "\n",
    "Como as amostras de sombreamento têm distribuição LogNormal, as seguintes propriedades são válidas:\n",
    "\n",
    "\\begin{equation}\n",
    "    Se\\quad Z = \\alpha A + \\beta B,\\quad ent{\\tilde a}o \\quad \\sigma _Z^{2}  = \\alpha^{2}\\sigma _A^{2}  + \\beta^{2}\\sigma _B^{2},\n",
    "\\end{equation}\n",
    "\n",
    "com $\\alpha$ e $\\beta$ constantes, $A$ e $B$ variáveis aleatórias com distribuição Gaussiana.\n",
    "\n",
    "Essa relação é usada para calcular o desvio padrão da amostra ($X_{u}$) em um ponto $u$, como ilustrada no carrinho amarelo da figura, obtendo a equação:\n",
    "\n",
    "\\begin{equation}\n",
    "    \\sigma _{X_u }  = \\sigma _X \\sqrt {\\left( {1 - 2Y + 2Y^2 }\n",
    "    \\right)\\left( {1 - 2X + 2X^2 } \\right)},\n",
    "\\end{equation}\n",
    "\n",
    "sendo $\\sigma_X$, o desvio padrão planejado para o sombreamento (o desvio padrão de cada ponto de grade), e $\\sigma_{X_u}$, o desvio padrão calculado no ponto $u$. \n",
    "\n",
    "Nota-se que esse valor do desvio padrão no ponto $u$ é diferente que o especificado para cada ponto de grade individual, ou seja, o desvio padrão do sombreamento planejado. Portanto, a equação a seguir pode resolver esse problema.\n",
    "\n",
    "\\begin{equation}\n",
    "    X'u = \\frac{{Xu}}{{\\sqrt {\\left( {1 - 2X + 2X^2 } \\right)\\left( {1\n",
    "    - 2Y + 2Y^2 } \\right)} }}\n",
    "\\end{equation}\n",
    "\n",
    "A figura a seguir ilustra um dos mapas de amostras de sombreamento para uma ERB arbitrária, construído por meio das equações acima. Construir mapas como esses e intergrá-los no cálculo do sombreamento correlacionado é o objetivo desse hands-on.\n",
    "\n",
    "![figmapashadow](../FIGS/HD_01_MATLAB/figmapashadow.png)\n",
    "\n",
    "Esse modelo é mais realista que considerar amostras totalmente descorrelacionadas de sombreamento, representando com mais fidelidade o que acontece com a atenuação do sinal de RF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prática 03: Construção dos mapas de sombreamento correlacionado\n",
    "\n",
    "Vamos escrever um código para construir um mapa de sombreamento correlacionado, incluindo a visualização espacial da distribuição dos pontos de medição. \n",
    "\n",
    "**Passo 01:** Inspecione e insira o código a seguir no editor do Matlab (salve com o nome **handson2_p31.m**). Nesse código, vamos:\n",
    "\n",
    "- Criar e plotar o grid com os pontos de grade do sombreamento correlacionado de acordo com uma distância de correlação qualquer;\n",
    "- Criar uma regra para achar os quatro pontos de grade mais pŕoximos de um dado ponto de medição;\n",
    "- Plotar os quatro pontos de grade mais pŕoximos de um dado ponto de medição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = 0.54 e Y = 0.54\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close all;clear all;clc;\n",
    "% Entrada de parâmetros\n",
    "dR = 200;                                                  % Raio do Hexágono\n",
    "dShad = 50;                                                % Distância de descorrelação do shadowing\n",
    "dPasso = 7;                                                % Distância entre pontos de medição\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "dDimXOri = 5*dR;                                              % Dimensão X do grid\n",
    "dDimYOri = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "%\n",
    "% Matriz de referência com posição de cada ponto do grid (posição relativa ao canto inferior esquerdo)\n",
    "dDimY = ceil(dDimYOri+mod(dDimYOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimX = ceil(dDimXOri+mod(dDimXOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "[mtPosx,mtPosy] = meshgrid(0:dPasso:dDimX, 0:dPasso:dDimY);\n",
    "mtPontosMedicao = mtPosx + j*mtPosy;\n",
    "%\n",
    "% Ponto de medição alvo (vamos localiza-lo no novo grid e plotar os quatro pontos que o circundam) - escolhido ao acaso\n",
    "dshadPoint = mtPontosMedicao(12,12);\n",
    "%\n",
    "% Matriz de pontos equidistantes de dShad em dShad\n",
    "dDimYS = ceil(dDimYOri+mod(dDimYOri,dShad));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimXS = ceil(dDimXOri+mod(dDimXOri,dShad)); \n",
    "[mtPosxShad,mtPosyShad] = meshgrid(0:dShad:dDimXS, 0:dShad:dDimYS);\n",
    "mtPosShad = mtPosxShad+j*mtPosyShad;\n",
    "%\n",
    "% Achar a posição do ponto de medição na matriz de shadowing correlacionado\n",
    "dXIndexP1 = real(dshadPoint)/dShad;\n",
    "dYIndexP1 = imag(dshadPoint)/dShad;\n",
    "%\n",
    "% Cálculo dos demais pontos depende de:\n",
    "%   (i) se o ponto de medição é um ponto de shadowing descorrelacionado\n",
    "%   (i) se o ponto está na borda lateral direita do grid e no canto superior do grid;\n",
    "%   (ii) se o ponto está na borda lateral direita do grid;\n",
    "%   (iii) se o ponto está na borda superior do grid;\n",
    "%   (iv)  se o ponto está no meio do grid.\n",
    "if (mod(dXIndexP1,1) == 0 && mod(dYIndexP1,1) == 0)\n",
    "    % O ponto de medição é um ponto de grade\n",
    "    dXIndexP1 = floor(dXIndexP1)+1;\n",
    "    dYIndexP1 = floor(dYIndexP1)+1;\n",
    "    plot(complex(mtPosShad(dYIndexP1,dXIndexP1)),'g*');\n",
    "    disp('O ponto de medição é um ponto de grade');\n",
    "else\n",
    "    % Índice na matriz do primeiro ponto próximo\n",
    "    dXIndexP1 = floor(dXIndexP1)+1;\n",
    "    dYIndexP1 = floor(dYIndexP1)+1;\n",
    "    if (dXIndexP1 == size(mtPosyShad,2)  && dYIndexP1 == size(mtPosyShad,1) )\n",
    "        % Ponto de medição está na borda da lateral direta do grid\n",
    "        % e no canto superior\n",
    "        % P2 - P1\n",
    "        % |    |\n",
    "        % P4 - P3\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1-1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        dXIndexP4 = dXIndexP1-1;\n",
    "        dYIndexP4 = dYIndexP1-1;\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1-1;\n",
    "        %\n",
    "    elseif (dXIndexP1 == size(mtPosyShad,2))\n",
    "        % Ponto de medição está na borda da lateral direta do grid\n",
    "        % P4 - P3\n",
    "        % |    |\n",
    "        % P2 - P1\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1-1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        dXIndexP4 = dXIndexP1-1;\n",
    "        dYIndexP4 = dYIndexP1+1;\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1+1;\n",
    "    elseif (dYIndexP1 == size(mtPosyShad,1))\n",
    "        % Ponto de medição está na borda superior do grid\n",
    "        % P1 - P2\n",
    "        % |    |\n",
    "        % P3 - P4\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1+1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        %\n",
    "        dXIndexP4 = dXIndexP1+1;\n",
    "        dYIndexP4 = dYIndexP1-1;\n",
    "        %\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1-1;\n",
    "        %\n",
    "    else\n",
    "        % P4 - P3\n",
    "        % |    |\n",
    "        % P1 - P2\n",
    "        %\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1+1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        %\n",
    "        dXIndexP4 = dXIndexP1+1;\n",
    "        dYIndexP4 = dYIndexP1+1;\n",
    "        %\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1+1;\n",
    "    end\n",
    "    %\n",
    "    % Plot dos pontos de grade\n",
    "    plot(complex(mtPosShad),'ko')\n",
    "    hold on;\n",
    "    %\n",
    "    % Plot do ponto de medição (quadrado vermelho)\n",
    "    plot(complex(dshadPoint),'sr')\n",
    "    %\n",
    "    % Plot dos quadtro pontos de grade que circundam o ponto de medição\n",
    "    mt4Poitns = complex([mtPosShad(dYIndexP1,dXIndexP1)...\n",
    "        mtPosShad(dYIndexP2,dXIndexP2)...\n",
    "        mtPosShad(dYIndexP3,dXIndexP3) ...\n",
    "        mtPosShad(dYIndexP4,dXIndexP4)]);\n",
    "    plot(mt4Poitns,'b*');\n",
    "    axis equal;\n",
    "    %\n",
    "    % Zoom nos pontos próximos ao ponto investigado\n",
    "    axis([-2*dShad+real(mtPosShad(dYIndexP3,dXIndexP3))...\n",
    "        2*dShad+real(mtPosShad(dYIndexP4,dXIndexP4))...\n",
    "        -2*dShad+imag(mtPosShad(dYIndexP3,dXIndexP3))...\n",
    "        2*dShad+imag(mtPosShad(dYIndexP1,dXIndexP1))]);\n",
    "    %\n",
    "end\n",
    "% Distâncias para regressão linear\n",
    "dDistX = (mod(real(dshadPoint),dShad))/dShad;\n",
    "dDistY = (mod(imag(dshadPoint),dShad))/dShad;\n",
    "\n",
    "disp(['X = ' num2str(dDistX) ' e Y = ' num2str(dDistY)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A execução do código resulta em:**\n",
    "1. Um gráfico mostrando: \n",
    " - O ponto de medição como um quadrado vermelho; \n",
    " - Os quatros pontos de grade que circundam os ponto de medição como asteriscos azuis; \n",
    "2. Esse código pode ser usado para verificar se os pontos de grade estão sendo identificados corretamente. Execute o código mudando a linha **dshadPoint = mtPontosMedicao(12,12)** para:\n",
    " - **dshadPoint = mtPontosMedicao(1,1)** \n",
    " - **dshadPoint = mtPontosMedicao(1,end)** \n",
    " - **dshadPoint = mtPontosMedicao(end,1)** \n",
    " - **dshadPoint = mtPontosMedicao(end,end)** \n",
    "\n",
    "\n",
    "** Analise o código com cuidado. Tente compreender a modelagem e a sintaxe usada. Discuta com os colegas. Faça um debug usando a IDE do Matlab.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 02:** Inspecione, insira o código a seguir no editor do Matlab (salve com o nome **handson2_p32.m**). Nesse código, vamos:\n",
    "\n",
    "- Sortear os pontos de sombreamento para os pontos de grade. A variável **dSigmaShad** define o desvio padrão do sombreamento lognormal criado;\n",
    "- Coletar amostras de sombreamento dos quatro pontos de grade mais pŕoximos de um dado ponto de medição (ou o valor da amostra independente, caso o ponto de medição coincida com um ponto de grade);\n",
    "- Calcular sobreamento via regressão linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = 0.54 e Y = 0.54\n",
      "O Sombreamento é 6.5319 dB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close all;clear all;clc;\n",
    "% Entrada de parâmetros\n",
    "dR = 200;                                                  % Raio do Hexágono\n",
    "dShad = 50;                                                % Distância de descorrelação do shadowing\n",
    "dPasso = 7;                                                % Distância entre pontos de medição\n",
    "dSigmaShad = 8;                                            % Desvio padrão do sombreamento lognormal\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "dDimXOri = 5*dR;                                              % Dimensão X do grid\n",
    "dDimYOri = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "%\n",
    "% Matriz de referência com posição de cada ponto do grid (posição relativa ao canto inferior esquerdo)\n",
    "dDimY = ceil(dDimYOri+mod(dDimYOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimX = ceil(dDimXOri+mod(dDimXOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "[mtPosx,mtPosy] = meshgrid(0:dPasso:dDimX, 0:dPasso:dDimY);\n",
    "mtPontosMedicao = mtPosx + j*mtPosy;\n",
    "%\n",
    "% Ponto de medição alvo (vamos localiza-lo no novo grid e plotar os quatro pontos que o circundam) - escolhido ao acaso\n",
    "dshadPoint = mtPontosMedicao(12,12);\n",
    "%\n",
    "% Matriz de pontos equidistantes de dShad em dShad\n",
    "dDimYS = ceil(dDimYOri+mod(dDimYOri,dShad));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimXS = ceil(dDimXOri+mod(dDimXOri,dShad)); \n",
    "[mtPosxShad,mtPosyShad] = meshgrid(0:dShad:dDimXS, 0:dShad:dDimYS);\n",
    "mtPosShad = mtPosxShad+j*mtPosyShad;\n",
    "% Amostras de sombremento para os pontos de grade\n",
    "mtShadowingSamples = dSigmaShad*randn(size(mtPosyShad));\n",
    "%\n",
    "% Achar a posição do ponto de medição na matriz de shadowing correlacionado\n",
    "dXIndexP1 = real(dshadPoint)/dShad;\n",
    "dYIndexP1 = imag(dshadPoint)/dShad;\n",
    "%\n",
    "% Cálculo dos demais pontos depende de:\n",
    "%   (i) se o ponto de medição é um ponto de shadowing descorrelacionado\n",
    "%   (i) se o ponto está na borda lateral direita do grid e no canto superior do grid;\n",
    "%   (ii) se o ponto está na borda lateral direita do grid;\n",
    "%   (iii) se o ponto está na borda superior do grid;\n",
    "%   (iv)  se o ponto está no meio do grid.\n",
    "if (mod(dXIndexP1,1) == 0 && mod(dYIndexP1,1) == 0)\n",
    "    % O ponto de medição é um ponto de grade\n",
    "    dXIndexP1 = floor(dXIndexP1)+1;\n",
    "    dYIndexP1 = floor(dYIndexP1)+1;\n",
    "    plot(complex(mtPosShad(dYIndexP1,dXIndexP1)),'g*');\n",
    "    disp('O ponto de medição é um ponto de grade');\n",
    "    % Amostra de sombreamento\n",
    "    dShadowingC = mtShadowingSamples(dYIndexP1,dXIndexP1);\n",
    "else\n",
    "    % Índice na matriz do primeiro ponto próximo\n",
    "    dXIndexP1 = floor(dXIndexP1)+1;\n",
    "    dYIndexP1 = floor(dYIndexP1)+1;\n",
    "    if (dXIndexP1 == size(mtPosyShad,2)  && dYIndexP1 == size(mtPosyShad,1) )\n",
    "        % Ponto de medição está na borda da lateral direta do grid\n",
    "        % e no canto superior\n",
    "        % P2 - P1\n",
    "        % |    |\n",
    "        % P4 - P3\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1-1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        dXIndexP4 = dXIndexP1-1;\n",
    "        dYIndexP4 = dYIndexP1-1;\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1-1;\n",
    "        %\n",
    "    elseif (dXIndexP1 == size(mtPosyShad,2))\n",
    "        % Ponto de medição está na borda da lateral direta do grid\n",
    "        % P4 - P3\n",
    "        % |    |\n",
    "        % P2 - P1\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1-1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        dXIndexP4 = dXIndexP1-1;\n",
    "        dYIndexP4 = dYIndexP1+1;\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1+1;\n",
    "    elseif (dYIndexP1 == size(mtPosyShad,1))\n",
    "        % Ponto de medição está na borda superior do grid\n",
    "        % P1 - P2\n",
    "        % |    |\n",
    "        % P3 - P4\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1+1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        %\n",
    "        dXIndexP4 = dXIndexP1+1;\n",
    "        dYIndexP4 = dYIndexP1-1;\n",
    "        %\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1-1;\n",
    "        %\n",
    "    else\n",
    "        % P4 - P3\n",
    "        % |    |\n",
    "        % P1 - P2\n",
    "        %\n",
    "        %\n",
    "        dXIndexP2 = dXIndexP1+1;\n",
    "        dYIndexP2 = dYIndexP1;\n",
    "        %\n",
    "        dXIndexP4 = dXIndexP1+1;\n",
    "        dYIndexP4 = dYIndexP1+1;\n",
    "        %\n",
    "        dXIndexP3 = dXIndexP1;\n",
    "        dYIndexP3 = dYIndexP1+1;\n",
    "    end\n",
    "    %\n",
    "    % Plot dos pontos de grade\n",
    "    plot(complex(mtPosShad),'ko')\n",
    "    hold on;\n",
    "    %\n",
    "    % Plot do ponto de medição (quadrado vermelho)\n",
    "    plot(complex(dshadPoint),'sr')\n",
    "    %\n",
    "    % Plot dos quadtro pontos de grade que circundam o ponto de medição\n",
    "    mt4Poitns = complex([mtPosShad(dYIndexP1,dXIndexP1)...\n",
    "        mtPosShad(dYIndexP2,dXIndexP2)...\n",
    "        mtPosShad(dYIndexP3,dXIndexP3) ...\n",
    "        mtPosShad(dYIndexP4,dXIndexP4)]);\n",
    "    plot(mt4Poitns,'b*');\n",
    "    axis equal;\n",
    "    %\n",
    "    % Zoom nos pontos próximos ao ponto investigado\n",
    "    axis([-2*dShad+real(mtPosShad(dYIndexP3,dXIndexP3))...\n",
    "        2*dShad+real(mtPosShad(dYIndexP4,dXIndexP4))...\n",
    "        -2*dShad+imag(mtPosShad(dYIndexP3,dXIndexP3))...\n",
    "        2*dShad+imag(mtPosShad(dYIndexP1,dXIndexP1))]);\n",
    "    %\n",
    "    % Distâncias para regressão linear\n",
    "    dDistX = (mod(real(dshadPoint),dShad))/dShad;\n",
    "    dDistY = (mod(imag(dshadPoint),dShad))/dShad;\n",
    "    disp(['X = ' num2str(dDistX) ' e Y = ' num2str(dDistY)])\n",
    "    % Ajuste do desvio padrão devido a regressão linear\n",
    "    dStdNormFactor = sqrt( (1 - 2 * dDistY + 2 * (dDistY^2) )*(1 - 2 * dDistX + 2 * (dDistX^2) ) );\n",
    "    %\n",
    "    % Amostras do sombreamento para os quatro pontos de grade\n",
    "    dSample1 = mtShadowingSamples(dYIndexP1,dXIndexP1);\n",
    "    dSample2 = mtShadowingSamples(dYIndexP2,dXIndexP2);\n",
    "    dSample3 = mtShadowingSamples(dYIndexP3,dXIndexP3);\n",
    "    dSample4 = mtShadowingSamples(dYIndexP4,dXIndexP4);\n",
    "    dShadowingC = ( (1-dDistY)*[dSample1*(1-dDistX) + dSample2*(dDistX)] +...\n",
    "        (dDistY)*[dSample3*(1-dDistX) + dSample4*(dDistX)])/dStdNormFactor;\n",
    "end\n",
    "disp(['O Sombreamento é ' num2str(dShadowingC) ' dB'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 03:** Inspecione, insira o código a seguir no editor do Matlab (salve com o nome **handson2_p33.m**). Nesse código, vamos:\n",
    "\n",
    "- Calcular o sobreamento via regressão linear para todos os pontos de medição;\n",
    "- Remover gráfico de pontos e display de mensagens;\n",
    "- Mostrar a atenuação por sombreamento em um gráfico 3D. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close all;clear all;clc;\n",
    "% Entrada de parâmetros\n",
    "dR = 150;                                                  % Raio do Hexágono\n",
    "dShad = 50;                                                % Distância de descorrelação do shadowing\n",
    "dPasso = 7;                                                % Distância entre pontos de medição\n",
    "dSigmaShad = 8;                                            % Desvio padrão do sombreamento lognormal\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "dDimXOri = 5*dR;                                              % Dimensão X do grid\n",
    "dDimYOri = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "%\n",
    "% Matriz de referência com posição de cada ponto do grid (posição relativa ao canto inferior esquerdo)\n",
    "dDimY = ceil(dDimYOri+mod(dDimYOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimX = ceil(dDimXOri+mod(dDimXOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "[mtPosx,mtPosy] = meshgrid(0:dPasso:dDimX, 0:dPasso:dDimY);\n",
    "mtPontosMedicao = mtPosx + j*mtPosy;\n",
    "%\n",
    "% Matriz de pontos equidistantes de dShad em dShad\n",
    "dDimYS = ceil(dDimYOri+mod(dDimYOri,dShad));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimXS = ceil(dDimXOri+mod(dDimXOri,dShad));\n",
    "[mtPosxShad,mtPosyShad] = meshgrid(0:dShad:dDimXS, 0:dShad:dDimYS);\n",
    "mtPosShad = mtPosxShad+j*mtPosyShad;\n",
    "% Amostras de sombremento para os pontos de grade\n",
    "mtShadowingSamples = dSigmaShad*randn(size(mtPosyShad));\n",
    "\n",
    "[dSizel, dSizec] = size(mtPontosMedicao);\n",
    "\n",
    "for il = 1: dSizel\n",
    "    for ic = 1: dSizec\n",
    "        %\n",
    "        % Ponto de medição alvo (vamos localiza-lo no novo grid e plotar os quatro pontos que o circundam) - escolhido ao acaso\n",
    "        dshadPoint = mtPontosMedicao(il,ic);\n",
    "        %\n",
    "        % Achar a posição do ponto de medição na matriz de shadowing correlacionado\n",
    "        dXIndexP1 = real(dshadPoint)/dShad;\n",
    "        dYIndexP1 = imag(dshadPoint)/dShad;\n",
    "        %\n",
    "        % Cálculo dos demais pontos depende de:\n",
    "        %   (i) se o ponto de medição é um ponto de shadowing descorrelacionado\n",
    "        %   (i) se o ponto está na borda lateral direita do grid e no canto superior do grid;\n",
    "        %   (ii) se o ponto está na borda lateral direita do grid;\n",
    "        %   (iii) se o ponto está na borda superior do grid;\n",
    "        %   (iv)  se o ponto está no meio do grid.\n",
    "        if (mod(dXIndexP1,1) == 0 && mod(dYIndexP1,1) == 0)\n",
    "            % O ponto de medição é um ponto de grade\n",
    "            dXIndexP1 = floor(dXIndexP1)+1;\n",
    "            dYIndexP1 = floor(dYIndexP1)+1;\n",
    "            plot(complex(mtPosShad(dYIndexP1,dXIndexP1)),'g*');\n",
    "            % Amostra de sombreamento\n",
    "            mtShadowingCorr(il,ic) = mtShadowingSamples(dYIndexP1,dXIndexP1);\n",
    "        else\n",
    "            % Índice na matriz do primeiro ponto próximo\n",
    "            dXIndexP1 = floor(dXIndexP1)+1;\n",
    "            dYIndexP1 = floor(dYIndexP1)+1;\n",
    "            if (dXIndexP1 == size(mtPosyShad,2)  && dYIndexP1 == size(mtPosyShad,1) )\n",
    "                % Ponto de medição está na borda da lateral direta do grid\n",
    "                % e no canto superior\n",
    "                % P2 - P1\n",
    "                % |    |\n",
    "                % P4 - P3\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1-1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                dXIndexP4 = dXIndexP1-1;\n",
    "                dYIndexP4 = dYIndexP1-1;\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1-1;\n",
    "                %\n",
    "            elseif (dXIndexP1 == size(mtPosyShad,2))\n",
    "                % Ponto de medição está na borda da lateral direta do grid\n",
    "                % P4 - P3\n",
    "                % |    |\n",
    "                % P2 - P1\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1-1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                dXIndexP4 = dXIndexP1-1;\n",
    "                dYIndexP4 = dYIndexP1+1;\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1+1;\n",
    "            elseif (dYIndexP1 == size(mtPosyShad,1))\n",
    "                % Ponto de medição está na borda superior do grid\n",
    "                % P1 - P2\n",
    "                % |    |\n",
    "                % P3 - P4\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1+1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                %\n",
    "                dXIndexP4 = dXIndexP1+1;\n",
    "                dYIndexP4 = dYIndexP1-1;\n",
    "                %\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1-1;\n",
    "                %\n",
    "            else\n",
    "                % P4 - P3\n",
    "                % |    |\n",
    "                % P1 - P2\n",
    "                %\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1+1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                %\n",
    "                dXIndexP4 = dXIndexP1+1;\n",
    "                dYIndexP4 = dYIndexP1+1;\n",
    "                %\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1+1;\n",
    "            end\n",
    "            %\n",
    "            % Distâncias para regressão linear\n",
    "            dDistX = (mod(real(dshadPoint),dShad))/dShad;\n",
    "            dDistY = (mod(imag(dshadPoint),dShad))/dShad;\n",
    "            % Ajuste do desvio padrão devido a regressão linear\n",
    "            dStdNormFactor = sqrt( (1 - 2 * dDistY + 2 * (dDistY^2) )*(1 - 2 * dDistX + 2 * (dDistX^2) ) );\n",
    "            %\n",
    "            % Amostras do sombreamento para os quatro pontos de grade\n",
    "            dSample1 = mtShadowingSamples(dYIndexP1,dXIndexP1);\n",
    "            dSample2 = mtShadowingSamples(dYIndexP2,dXIndexP2);\n",
    "            dSample3 = mtShadowingSamples(dYIndexP3,dXIndexP3);\n",
    "            dSample4 = mtShadowingSamples(dYIndexP4,dXIndexP4);\n",
    "            mtShadowingCorr(il,ic) = ( (1-dDistY)*[dSample1*(1-dDistX) + dSample2*(dDistX)] +...\n",
    "                (dDistY)*[dSample3*(1-dDistX) + dSample4*(dDistX)])/dStdNormFactor;\n",
    "        end\n",
    "    end\n",
    "end\n",
    "% Plot da superfície de atenuação devido ao sombreamento\n",
    "surf(mtPosx,mtPosy,mtShadowingCorr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nota que o sombreamento tem comportamento mais suave e não muda bruscamente para pontos próximos.\n",
    "\n",
    "** Analise o código com cuidado. Tente compreender a modelagem e a sintaxe usada. Discuta com os colegas. Faça um debug usando uma IDE de sua preferência.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prática 04: Cálculo e plot da potência recebida com sombreamento correlacionado\n",
    "\n",
    "amos escrever um código para o cálculo da potência recebida nos pontos de medição do REM de cada ERB, e também considerando a composição das 7 ERBs. Como especificado no hands-on 01, precisamos considerar que a potência recebida de cada ponto de medição é a maior potência recebida em relação as 7 ERBs.\n",
    "\n",
    "A ideia é calcular o REM para todo o grid, considerando três casos: (i) Somente path loss; (ii) Path loss e sombreamento descorrelacionado; e (iii) Path loss e sombreamento correlacionado. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 01:** Crie uma função chamada **fDrawDeploy.m** com o segunte código. Ela servirá para desenhar o grid celular. Tal função servira para teros certeza que o posicionamento das ERBs estão corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/gppcom/2020_5/DCO1020_2020_5/fDrawSector.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fDrawSector.m\n",
    "function fDrawSector(dR,dCenter)\n",
    "vtHex=zeros(1,0);\n",
    "for ie=1:6\n",
    "    vtHex=[vtHex dR*(cos((ie-1)*pi/3)+j*sin((ie-1)*pi/3))];\n",
    "end\n",
    "vtHex=vtHex+dCenter;\n",
    "vtHexp=[vtHex vtHex(1)];\n",
    "plot(vtHexp,'k');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fDrawSector(100,100+50*i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 02:** Crie uma função chamada **fDrawDeploy.m** com o segunte código. Ela servirá para desenhar o grid celular. Tal função servira para teros certeza que o posicionamento das ERBs estão corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/gppcom/2020_5/DCO1020_2020_5/fDrawDeploy.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fDrawDeploy.m\n",
    "function fDrawDeploy(dR,vtBs)\n",
    "% Desenha setores hexagonais\n",
    "hold on;\n",
    "for iBsD = 1 : length(vtBs)\n",
    "    fDrawSector(dR,vtBs(iBsD));\n",
    "end\n",
    "% Plot BSs\n",
    "plot(vtBs,'sk'); axis equal;\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 03:** Agora vamos criar uma função chamada **fCorrShadowing** com uma versão modificada do código que estamos trabalhando até o momento. Essa função vai realizar as seguintes funcionalidades:\n",
    "- Criar 8 mapas de atenuação de sombreamento (7 para as ERBs e 1 comum) e devolver um mapa do sombreameto de cada ponto de medição, considerando o valor do **dAlphaCorr** para controlar a correlação do sombreamento entre ERBs;\n",
    "\n",
    "Crie um arquivo  **fCorrShadowing.m** com seguinte código. **Atenção:** você precisa colocar o arquivo **fCorrShadowing.m** na pasta de trabalho do Matlab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/gppcom/2020_5/DCO1020_2020_5/fCorrShadowing.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fCorrShadowing.m\n",
    "function mtShadowingCorr = fCorrShadowing(mtPoints,dShad,dAlphaCorr,dSigmaShad,dDimXOri,dDimYOri)\n",
    "% INPUTS:\n",
    "%   mtPoints: Matriz de números complexos com os pontos de medição\n",
    "%   dShad: Distância de descorrelação do shadowing\n",
    "%   dSigmaShad: Desvio padrão do shadowing Lognormal\n",
    "%   dAlphaCorr: Coeficiente de correlação do sombreamento entre ERBs\n",
    "%   dDimXOri: Dimensão X do grid em metros\n",
    "%   dDimYOri: Dimensão Y do grid em metros\n",
    "%\n",
    "% Matriz de pontos equidistantes de dShad em dShad\n",
    "dDimYS = ceil(dDimYOri+mod(dDimYOri,dShad));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimXS = ceil(dDimXOri+mod(dDimXOri,dShad));\n",
    "[mtPosxShad,mtPosyShad] = meshgrid(0:dShad:dDimXS, 0:dShad:dDimYS);\n",
    "mtPosShad = mtPosxShad+j*mtPosyShad;\n",
    "%\n",
    "% Amostras de sombremento para os pontos de grade\n",
    "% Matrizes com amostras de shadowing independentes\n",
    "% 7 matrizes, uma cada cada ERB\n",
    "% 1 matriz para o ambiente\n",
    "for iMap = 1:8\n",
    "    mtShadowingSamples(:,:,iMap) = dSigmaShad*randn(size(mtPosyShad));\n",
    "end\n",
    "\n",
    "[dSizel, dSizec] = size(mtPoints);\n",
    "for il = 1: dSizel\n",
    "    for ic = 1: dSizec\n",
    "        %\n",
    "        % Ponto de medição alvo (vamos localiza-lo no novo grid e plotar os quatro pontos que o circundam) - escolhido ao acaso\n",
    "        dshadPoint = mtPoints(il,ic);\n",
    "        %\n",
    "        % Achar a posição do ponto de medição na matriz de shadowing correlacionado\n",
    "        dXIndexP1 = real(dshadPoint)/dShad;\n",
    "        dYIndexP1 = imag(dshadPoint)/dShad;\n",
    "        %\n",
    "        % Cálculo dos demais pontos depende de:\n",
    "        %   (i) se o ponto de medição é um ponto de shadowing descorrelacionado\n",
    "        %   (i) se o ponto está na borda lateral direita do grid e no canto superior do grid;\n",
    "        %   (ii) se o ponto está na borda lateral direita do grid;\n",
    "        %   (iii) se o ponto está na borda superior do grid;\n",
    "        %   (iv)  se o ponto está no meio do grid.\n",
    "        if (mod(dXIndexP1,1) == 0 && mod(dYIndexP1,1) == 0)\n",
    "            % O ponto de medição é um ponto de grade\n",
    "            dXIndexP1 = floor(dXIndexP1)+1;\n",
    "            dYIndexP1 = floor(dYIndexP1)+1;\n",
    "            % Amostra de sombreamento do ambiente\n",
    "            dShadowingC = mtShadowingSamples(dYIndexP1,dXIndexP1,8);\n",
    "            % Amostra do sombreamento de cada ERB\n",
    "            for iMap = 1:7\n",
    "                dShadowingERB = mtShadowingSamples(dYIndexP1,dXIndexP1,iMap);\n",
    "                mtShadowingCorr(il,ic,iMap) = sqrt(dAlphaCorr)*dShadowingC + sqrt(1-dAlphaCorr)*dShadowingERB;\n",
    "            end\n",
    "        else\n",
    "            % Índice na matriz do primeiro ponto próximo\n",
    "            dXIndexP1 = floor(dXIndexP1)+1;\n",
    "            dYIndexP1 = floor(dYIndexP1)+1;\n",
    "            if (dXIndexP1 == size(mtPosyShad,2)  && dYIndexP1 == size(mtPosyShad,1) )\n",
    "                % Ponto de medição está na borda da lateral direta do grid\n",
    "                % e no canto superior\n",
    "                % P2 - P1\n",
    "                % |    |\n",
    "                % P4 - P3\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1-1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                dXIndexP4 = dXIndexP1-1;\n",
    "                dYIndexP4 = dYIndexP1-1;\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1-1;\n",
    "                %\n",
    "            elseif (dXIndexP1 == size(mtPosyShad,2))\n",
    "                % Ponto de medição está na borda da lateral direta do grid\n",
    "                % P4 - P3\n",
    "                % |    |\n",
    "                % P2 - P1\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1-1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                dXIndexP4 = dXIndexP1-1;\n",
    "                dYIndexP4 = dYIndexP1+1;\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1+1;\n",
    "            elseif (dYIndexP1 == size(mtPosyShad,1))\n",
    "                % Ponto de medição está na borda superior do grid\n",
    "                % P1 - P2\n",
    "                % |    |\n",
    "                % P3 - P4\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1+1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                %\n",
    "                dXIndexP4 = dXIndexP1+1;\n",
    "                dYIndexP4 = dYIndexP1-1;\n",
    "                %\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1-1;\n",
    "                %\n",
    "            else\n",
    "                % P4 - P3\n",
    "                % |    |\n",
    "                % P1 - P2\n",
    "                %\n",
    "                %\n",
    "                dXIndexP2 = dXIndexP1+1;\n",
    "                dYIndexP2 = dYIndexP1;\n",
    "                %\n",
    "                dXIndexP4 = dXIndexP1+1;\n",
    "                dYIndexP4 = dYIndexP1+1;\n",
    "                %\n",
    "                dXIndexP3 = dXIndexP1;\n",
    "                dYIndexP3 = dYIndexP1+1;\n",
    "            end\n",
    "            %\n",
    "            % Distâncias para regressão linear\n",
    "            dDistX = (mod(real(dshadPoint),dShad))/dShad;\n",
    "            dDistY = (mod(imag(dshadPoint),dShad))/dShad;\n",
    "            % Ajuste do desvio padrão devido a regressão linear\n",
    "            dStdNormFactor = sqrt( (1 - 2 * dDistY + 2 * (dDistY^2) )*(1 - 2 * dDistX + 2 * (dDistX^2) ) );\n",
    "            %\n",
    "            % Amostra do sombreamento do mapa comum\n",
    "            dSample1 = mtShadowingSamples(dYIndexP1,dXIndexP1,8);\n",
    "            dSample2 = mtShadowingSamples(dYIndexP2,dXIndexP2,8);\n",
    "            dSample3 = mtShadowingSamples(dYIndexP3,dXIndexP3,8);\n",
    "            dSample4 = mtShadowingSamples(dYIndexP4,dXIndexP4,8);\n",
    "            dShadowingC = ( (1-dDistY)*[dSample1*(1-dDistX) + dSample2*(dDistX)] +...\n",
    "                (dDistY)*[dSample3*(1-dDistX) + dSample4*(dDistX)])/dStdNormFactor;\n",
    "            % Amostra do sombreamento de cada ERB\n",
    "            for iMap = 1:7\n",
    "                dSample1 = mtShadowingSamples(dYIndexP1,dXIndexP1,iMap);\n",
    "                dSample2 = mtShadowingSamples(dYIndexP2,dXIndexP2,iMap);\n",
    "                dSample3 = mtShadowingSamples(dYIndexP3,dXIndexP3,iMap);\n",
    "                dSample4 = mtShadowingSamples(dYIndexP4,dXIndexP4,iMap);\n",
    "                dShadowingERB = ( (1-dDistY)*[dSample1*(1-dDistX) + dSample2*(dDistX)] +...\n",
    "                    (dDistY)*[dSample3*(1-dDistX) + dSample4*(dDistX)])/dStdNormFactor;\n",
    "                mtShadowingCorr(il,ic,iMap) = sqrt(dAlphaCorr)*dShadowingC + sqrt(1-dAlphaCorr)*dShadowingERB;\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 04:** Inspecione, insira o código a seguir no editor do Matlab (salve com o nome **handson2_p41.m**). Nesse código, vamos:\n",
    "\n",
    "- Criar 8 mapas de atenuação de sombreamento (7 para as ERBs e 1 comum);\n",
    "- Incluir o parâmetro **dAlphaCorr** para controlar a correlação do sombreamento entre ERBs;\n",
    "- Remover gráfico da atenuação por sombreamento em um gráfico 3D;\n",
    "- Calcular REM para todo o grid, considerando três casos: (i) Somente path loss; (ii) Path loss e sombreamento descorrelacionado; e (iii) Path loss e sombreamento correlacionado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% Entrada de parâmetros\n",
    "dR = 200;                                                  % Raio do Hexágono\n",
    "dFc = 800;                                                 % Frequência da portadora\n",
    "dShad = 50;                                                % Distância de descorrelação do shadowing\n",
    "dSigmaShad = 8;                                            % Desvio padrão do sombreamento lognormal\n",
    "dAlphaCorr = 0.5;                                          % Coeficiente de correlação do sombreamento entre ERBs (sombreamento correlacionado)\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "%dPasso = ceil(dR/10);                                     % Resolução do grid: distância entre pontos de medição\n",
    "dPasso = 10;\n",
    "dRMin = dPasso;                                            % Raio de segurança\n",
    "dIntersiteDistance = 2*sqrt(3/4)*dR;                       % Distância entre ERBs (somente para informação)\n",
    "%\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "dDimXOri = 5*dR;                                           % Dimensão X do grid\n",
    "dDimYOri = 6*sqrt(3/4)*dR;                                 % Dimensão Y do grid\n",
    "dPtdBm = 57;                                               % EIRP (incluindo ganho e perdas) (https://pt.slideshare.net/naveenjakhar12/gsm-link-budget)\n",
    "dPtLinear = 10^(dPtdBm/10)*1e-3;                           % EIRP em escala linear\n",
    "dHMob = 5;                                                 % Altura do receptor\n",
    "dHBs = 30;                                                 % Altura do transmissor\n",
    "dAhm = 3.2*(log10(11.75*dHMob)).^2 - 4.97;                 % Modelo Okumura-Hata: Cidade grande e fc  >= 400MHz\n",
    "% Vetor com posições das BSs (grid Hexagonal com 7 células, uma célula central e uma camada de células ao redor)\n",
    "vtBs = [ 0 ];\n",
    "dOffset = pi/6;\n",
    "for iBs = 2 : 7\n",
    "    vtBs = [ vtBs dR*sqrt(3)*exp( j * ( (iBs-2)*pi/3 + dOffset ) ) ];\n",
    "end\n",
    "vtBs = vtBs + (dDimXOri/2 + j*dDimYOri/2);                        % Ajuste de posição das bases (posição relativa ao canto inferior esquerdo)\n",
    "%\n",
    "% Matriz de referência com posição de cada ponto do grid (posição relativa ao canto inferior esquerdo)\n",
    "dDimY = ceil(dDimYOri+mod(dDimYOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimX = ceil(dDimXOri+mod(dDimXOri,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "[mtPosx,mtPosy] = meshgrid(0:dPasso:dDimX, 0:dPasso:dDimY);\n",
    "mtPontosMedicao = mtPosx + j*mtPosy;\n",
    "% Iniciação da Matriz de com a pontência de recebida máxima em cada ponto\n",
    "% medido. Essa potência é a maior entre as 7 ERBs.\n",
    "mtPowerFinaldBm = -inf*ones(size(mtPosy));\n",
    "mtPowerFinalShaddBm = -inf*ones(size(mtPosy));\n",
    "mtPowerFinalShadCorrdBm = -inf*ones(size(mtPosy));\n",
    "%\n",
    "% Calcula o sombreamento correlacionado\n",
    "mtShadowingCorr = fCorrShadowing(mtPontosMedicao,dShad,dAlphaCorr,dSigmaShad,dDimXOri,dDimYOri);\n",
    "%\n",
    "% Calcular O REM de cada ERB e aculumar a maior potência em cada ponto de medição\n",
    "for iBsD = 1 : length(vtBs)                                 % Loop nas 7 ERBs\n",
    "    % Matriz 3D com os pontos de medição de cada ERB. Os pontos são\n",
    "    % modelados como números complexos X +jY, sendo X a posição na abcissa e Y, a posição no eixo das ordenadas\n",
    "    mtPosEachBS = mtPontosMedicao-(vtBs(iBsD));\n",
    "    mtDistEachBs = abs(mtPosEachBS);              % Distância entre cada ponto de medição e a sua ERB\n",
    "    mtDistEachBs(mtDistEachBs < dRMin) = dRMin;             % Implementação do raio de segurança\n",
    "    % Okumura-Hata (cidade urbana) - dB\n",
    "    mtPldB = 69.55 + 26.16*log10(dFc) + (44.9 - 6.55*log10(dHBs))*log10(mtDistEachBs/1e3) - 13.82*log10(dHBs) - dAhm;\n",
    "    % Shadowing independente em cada ponto\n",
    "    mtShadowing = dSigmaShad*randn(size(mtPosy));\n",
    "    % Potências recebidas em cada ponto de medição sem shadowing\n",
    "    mtPowerEachBSdBm = dPtdBm - mtPldB;\n",
    "    % Potências recebidas em cada ponto de medição com shadowing\n",
    "    mtPowerEachBSShaddBm = dPtdBm - mtPldB + mtShadowing;\n",
    "    % Potências recebidas em cada ponto de medição com shadowing\n",
    "    % correlacionado\n",
    "    mtPowerEachBSShadCorrdBm = dPtdBm - mtPldB + mtShadowingCorr(:,:,iBsD);\n",
    "    % Cálulo da maior potência em cada ponto de medição sem shadowing\n",
    "    mtPowerFinaldBm = max(mtPowerFinaldBm,mtPowerEachBSdBm);\n",
    "    % Cálulo da maior potência em cada ponto de medição com shadowing\n",
    "    mtPowerFinalShaddBm = max(mtPowerFinalShaddBm,mtPowerEachBSShaddBm);\n",
    "    % Cálulo da maior potência em cada ponto de medição com shadowing\n",
    "    mtPowerFinalShadCorrdBm = max(mtPowerFinalShadCorrdBm,mtPowerEachBSShadCorrdBm);\n",
    "end\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) sem shadowing\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinaldBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB sem shadowing']);\n",
    "%\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) sem shadowing\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinalShaddBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB com shadowing']);\n",
    "%\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) com shadowing\n",
    "% correlacionado\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinalShadCorrdBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB com shadowing correlacionado']);"
   ]
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    "**A execução do código resulta em:**\n",
    "1. Gráficos com o REM para todo o grid, considerando três casos: \n",
    "\n",
    " - Somente path loss; \n",
    " - Path loss e sombreamento descorrelacionado; \n",
    " - Path loss e sombreamento correlacionado. \n",
    " \n",
    "Note que o caso com sombreamento correlacionado apresenta a distribiução mais uniforme dos níveis de potência, quando comparamos com o caso do sombreamento descorrelacionado. \n",
    "\n",
    "** Analise o código com cuidado. Tente compreender a modelagem e a sintaxe usada. Discuta com os colegas. Faça um debug usando a IDE do Matlab.**"
   ]
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   "source": [
    "##  Entrega 3: Comprovação do fator de ajuste do desvio padrão do sombreamento correlacionado\n",
    "\n",
    "Escreva um código para comprovar que o desvio padrão das amostras do sombreamento correlacionado tem o mesmo desvio padrão de entrada **dSigmaShad**. Comprovar que isso é verdade independente do valor de **dAlphaCorr**.\n",
    "\n",
    "Verificar se as amostras **mtShadowingCorr** tem sombreamento com desvio padrão **dSigmaShad** para alguns valores de **dAlphaCorr**.\n",
    "\n",
    "**Atenção:** Caso o valor não bate, tente modificar o código para corrigir o problema (a priori, ele não existe).\n"
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    "##  Entrega 4: Modelagem e avaliação da inclusão de microcélulas\n",
    "\n",
    "\n",
    "Uma solução tecnológica para tratar problemas de cobertura é o uso de repetidores (microcélulas, picocélulas) com intuito de estender o alcance de uma estrutura macrocelular (torres altas e com alta potência). Considere o uso de microcélulas com as seguintes características:\n",
    "\n",
    "  - Potência de transmissão: 0.1 W\n",
    "  - Perda de percurso: PL = 55 + 38 $\\cdot$ log$_{10}$(d)+ (24.5 + $\\frac{1.5*f}{925}$)*log$_{10}$(f), com d em km e f em MHz;\n",
    "   \n",
    "Seu alvo é analisar 3 cenários:\n",
    "\n",
    "  - (i) Sistema somente com perda de percurso (sem shadowing), f = 800 MHz, ht = 32m e hr= 1,5m, raio do hexágono 500m;\n",
    "  - (ii) Sistema somente com perda de percurso (sem shadowing), f = 1800 MHz, ht = 32m e hr= 1,5m, raio do hexágono 500m;\n",
    "  - (iii)Sistema somente com perda de percurso (sem shadowing), f = 2100 MHz, ht = 32m e hr= 1,5m, raio do hexágono 500m;\n",
    "  \n",
    "\n",
    "Prepare um primeiro conjunto de resultados, ainda sem microcélulas, e com a potência da macrocélula igual a 21 dBm. Eles são:\n",
    "\n",
    " - REMs com somente de duas cores, identificando: (i) a área de Outage do mapa (cor 1); e (ii) área com potência maior que a mínima (cor 2). Fazer 3 REMs, uma para cada cenário. Analise esses gráficos e decida qual o melhor posicionamento da suas seis microcélulas. Inclua esses REMs no seu relatório. \n",
    " \n",
    " - Discuta se as áreas de outage mudaram, e se essa mudança a fez escolher posicionamento diferentes para microcélulas, dependendo da frequência da portadora;\n",
    "\n",
    "De posse do primeiro conjunto de resultados, posicione seis (somente seis) microcélulas nos pontos estratégicos escolhidos por você. Deve ficar claro no seu relatório a razão do posicionamento escolhido. \n",
    "\n",
    "O sistema não é multi-conectividade, i.e., a potência de um ponto NÃO é a SOMA da potência da melhor macrocélula com a microcélula. Assim, como um usuário está conectado ou a macrocélula ou a microcélula, ao final você terá somente uma matriz de potências recebidas.\n",
    "\n",
    "A figura a seguir mostra um exemplo de posicionamento de seis microcélulas. Será que algumas dessas posições são realmente as mais aconselhadas para resolver o problema da Outage?\n",
    "\n",
    "![fig_shadcorr_mod_zoom_300](../FIGS/HD_01_MATLAB/microcelulas.png)\n",
    "\n",
    "Agora com as microcélulas inteligentemente posicionadas por você, faça um segundo conjutno de resultados com:\n",
    "\n",
    " - Os mesmos 3 REMs, mas agora com a inclusão das microcélulas (um REM para cada frequência da portadora). Compare os REMS com e sem microcélula para cada cenário;\n",
    " - Fazer um gráfico de barras (pode ser no Microsoft Excel, por exemplo) da outage com e sem microcélula para cada cenário.\n",
    " \n",
    " \n",
    "Considere -90 dBm como potência mínima de operação, i.e., um usuário (ou ponto no grid) é bloqueado se sua potência recebida for menor que esse valor. Outra consideração importante é que os usuários estão conectados a melhor estação rádio base. Faça os mapas com resolução espacial de 50 m. Se necessário, essa resolução pode ser diminuída, para melhor visualização da cobertura das microcélulas.\n",
    "\n",
    "Compare os resultados com a situação sem a microcélulas. Quais as principais observações e diferenças? inclua figuras e discussão no seu relatório."
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